E affinis 16S analysis

Martin Bontrager

Abundance Bar Plots

I have transformed the data by relative abundance. I am plotting only the most abundance phyla and the composition of the phyla.

Abundance by Phylum

Test for unequal variances at the phylum level

## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 1.1898, num df = 13, denom df = 13, p-value = 0.7587
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.3819612 3.7063408
## sample estimates:
## ratio of variances 
##           1.189823 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 1.2424, num df = 13, denom df = 13, p-value = 0.7014
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.3988259 3.8699859
## sample estimates:
## ratio of variances 
##           1.242357 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 0.60575, num df = 13, denom df = 13, p-value = 0.3778
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.1944614 1.8869458
## sample estimates:
## ratio of variances 
##          0.6057541 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 0.39285, num df = 13, denom df = 13, p-value = 0.1043
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.1261126 1.2237268
## sample estimates:
## ratio of variances 
##          0.3928452 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 9.3538, num df = 13, denom df = 13, p-value = 0.0002779
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##   3.002786 29.137372
## sample estimates:
## ratio of variances 
##           9.353784 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 4.3167, num df = 13, denom df = 13, p-value = 0.01295
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##   1.385753 13.446581
## sample estimates:
## ratio of variances 
##            4.31667 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 0.35141, num df = 13, denom df = 13, p-value = 0.07021
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.1128096 1.0946421
## sample estimates:
## ratio of variances 
##          0.3514059 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 2.1902, num df = 13, denom df = 13, p-value = 0.1708
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.7031007 6.8225006
## sample estimates:
## ratio of variances 
##           2.190184 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 0.19041, num df = 13, denom df = 13, p-value = 0.005274
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.0611274 0.5931465
## sample estimates:
## ratio of variances 
##           0.190414 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 0.14914, num df = 13, denom df = 13, p-value = 0.001594
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.04787843 0.46458586
## sample estimates:
## ratio of variances 
##           0.149143

Abundance by phylum in merged samples

## png 
##   2

Abundance by Class

## png 
##   2

## png 
##   2

Test unequal variance in Classes

## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 2.0686, num df = 13, denom df = 13, p-value = 0.1017
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  0.8027428       Inf
## sample estimates:
## ratio of variances 
##            2.06861 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 3.6258, num df = 13, denom df = 13, p-value = 0.01365
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  1.407019      Inf
## sample estimates:
## ratio of variances 
##           3.625785 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 9.9177, num df = 13, denom df = 13, p-value = 0.000101
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  3.848648      Inf
## sample estimates:
## ratio of variances 
##           9.917684 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 1.9709, num df = 13, denom df = 13, p-value = 0.1172
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  0.7648359       Inf
## sample estimates:
## ratio of variances 
##           1.970926 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 5.6892, num df = 13, denom df = 13, p-value = 0.001798
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  2.207744      Inf
## sample estimates:
## ratio of variances 
##           5.689196 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 13.079, num df = 13, denom df = 13, p-value = 2.147e-05
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  5.075521      Inf
## sample estimates:
## ratio of variances 
##           13.07925 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 11.266, num df = 13, denom df = 13, p-value = 4.987e-05
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  4.372062      Inf
## sample estimates:
## ratio of variances 
##           11.26648 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 2.8814, num df = 13, denom df = 13, p-value = 0.03353
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  1.118151      Inf
## sample estimates:
## ratio of variances 
##           2.881395 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 1.0994, num df = 13, denom df = 13, p-value = 0.4334
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  0.4266429       Inf
## sample estimates:
## ratio of variances 
##           1.099428 
## 
## 
##  F test to compare two variances
## 
## data:  c[, i] and w[, i]
## F = 4.6949, num df = 13, denom df = 13, p-value = 0.004438
## alternative hypothesis: true ratio of variances is greater than 1
## 95 percent confidence interval:
##  1.821902      Inf
## sample estimates:
## ratio of variances 
##           4.694907

## 
##  Welch Two Sample t-test
## 
## data:  a and b
## t = 2.3163, df = 11.473, p-value = 0.01997
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
##  0.003095143         Inf
## sample estimates:
##   mean of x   mean of y 
## 0.018179730 0.004579922

Abundance by Order

Abundance by Family

Abundance by genus

Without common phlya

Now I think it might be interesting to look at these plots excluding all actinobacteria, proteobacteria, and bacteroides (The vast majority of taxa belong to those phyla)